Rubberized geopolymer composites: A comprehensive review

SMA Qaidi, AS Mohammed, HU Ahmed, RH Faraj… - Ceramics …, 2022 - Elsevier
The construction sector has been addressing the issue of integrating sustainability into
production processes over the last few years, either through solid waste materials as …

Application of bio and nature-inspired algorithms in agricultural engineering

C Maraveas, PG Asteris, KG Arvanitis… - … Methods in Engineering, 2023 - Springer
The article reviewed the four major Bioinspired intelligent algorithms for agricultural
applications, namely ecological, swarm-intelligence-based, ecology-based, and multi …

Prediction of concrete materials compressive strength using surrogate models

W Emad, AS Mohammed, R Kurda, K Ghafor… - Structures, 2022 - Elsevier
Using soft computing methods could be of great interest in predicting the compressive
strength of Ultra-High-Performance Fibre Reinforced Concrete (UHPFRC). Therefore, this …

Convolution-based ensemble learning algorithms to estimate the bond strength of the corroded reinforced concrete

L Cavaleri, MS Barkhordari, CC Repapis… - … and Building Materials, 2022 - Elsevier
Reinforced concrete bond strength deterioration is one of the most serious problems in the
construction industry. It is one of the most common factors impacting structural deterioration …

Predicting uniaxial compressive strength of rocks using ANN models: incorporating porosity, compressional wave velocity, and schmidt hammer data

PG Asteris, M Karoglou, AD Skentou, G Vasconcelos… - Ultrasonics, 2024 - Elsevier
The unconfined compressive strength (UCS) of intact rocks is crucial for engineering
applications, but traditional laboratory testing is often impractical, especially for historic …

[HTML][HTML] Estimating compressive strength of concrete containing rice husk ash using interpretable machine learning-based models

M Alyami, M Khan, AWA Hammad… - Case Studies in …, 2024 - Elsevier
The construction sector is a major contributor to global greenhouse gas emissions. Using
recycled and waste materials in concrete is a practical solution to address environmental …

Metamodel techniques to estimate the compressive strength of UHPFRC using various mix proportions and a high range of curing temperatures

W Emad, AS Mohammed, A Bras, PG Asteris… - … and Building Materials, 2022 - Elsevier
In order to predict the compressive strength (σ c) of Ultra-high performance fiber reinforced
concrete (UHPFRC), develo** a reliable and precise technique based on all main …

[HTML][HTML] The use of machine learning techniques to investigate the properties of metakaolin-based geopolymer concrete

SAE Afzali, MA Shayanfar, M Ghanooni-Bagha… - Journal of Cleaner …, 2024 - Elsevier
The construction industry significantly contributes to global greenhouse gas emissions,
highlighting the imperative for develo** environmentally friendly construction materials …

Machine learning models for predicting the compressive strength of concrete containing nano silica

A Garg, P Aggarwal, Y Aggarwal… - Computers and …, 2022 - koreascience.kr
Experimentally predicting the compressive strength (CS) of concrete (for a mix design) is a
time-consuming and laborious process. The present study aims to propose surrogate …

Develo** bearing capacity model for geogrid-reinforced stone columns improved soft clay utilizing MARS-EBS hybrid method

AR Ghanizadeh, A Ghanizadeh, PG Asteris… - Transportation …, 2023 - Elsevier
Because of the complicated geometry and a lack of knowledge about the parameters that
impact it, estimating the ultimate bearing capacity (q rs) of a geogrid-reinforced sandy bed …